Chart patterns are frequently used by financial analysts for predicting price trends in stock markets. Identifying chart patterns from historical price data can be regarded as a subsequence pattern-matching problem in financial time series data mining. A two-phase method is commonly used for subsequence pattern-matching, which includes segmentation of the time series and similarity calculation between subsequences and the template patterns. In this paper, we propose a novel approach for locating chart patterns in financial time series. In this approach, we extend the subsequence search algorithm UCR Suite with a Support Vector Machine (SVM) to train a classifier for chart pattern-matching. The experimental results show that our approach has achieved significant improvement over other methods in terms of speed and accuracy.